Assessing the Credibility of Decisional Guidance Delivered by Information Systems

Abstract

This work investigates the independent and joint influences of suggestive guidance and credibility indicators in a repeated choice environment laden with risk and uncertainty. Consistent with expectations, results from two studies reveal that aggressive and conservative suggestive guidance influence participant decision making. Credibility indicators partially moderate these relationships such that low credibility indicators lead to increased risk taking when suggestive guidance is conservative and decreased risk taking when suggestive guidance is aggressive. Post hoc analyses designed to examine differences in results across the two studies reveal unexpected differences in risk trends across participant type. Together, these findings contribute to the decision support system (DSS) literature by illustrating how credibility indicators can push users toward or away from either beneficial or detrimental suggestive guidance, and further, by challenging the commonly held assumption that inexperienced and experienced decision makers accept similar levels of risk in DSS settings where risk and uncertainty prevail.

Key words and phrases:

• computer-supported decision making
• credibility indicators
• decision biases
• decision guidance
• decision support systems

Notes

^1. The choice of $90,000 also maximized ecological validity. At the end of 2013, 401(k) accounts averaged $89,300 [37].

^2. An existing risk tolerance scale was used [26]. Reliability exceeded acceptable limits (α = .715).

^3. Note that although the main effect of credibility indicators was included in the statistical model, the variable cannot be conceptually evaluated independently of the suggestive guidance it references. Thus, the main effect was not hypothesized or discussed in the results section.

^4. The majority of the MLA-based decision-making studies use a hypothetical lottery (see [13] for a review).

^5. As a reminder, prior period performance was significant for students and not retirement-plan participants. This finding was one factor used to determine the need for the post hoc, repeated measures analyses.

^6. Note that although findings from the current study may be applicable to recommendation agents research, the information system used in the current study does not alter recommendations based on user inputs and therefore would not be considered a recommendation agent–based system.

Additional information

Notes on contributors

Andrew Hardin

Andrew Hardin ([email protected]; corresponding author) is a professor of information systems and associate dean for research and graduate programs in the Lee Business School at the University of Nevada, Las Vegas. His research focuses on organizational collaboration and virtual work, technology-mediated financial decision making, and research methodologies. His work appears in Journal of Management Information Systems, Management Science, MIS Quarterly, Organizational Behavior and Human Decision Processes, and Journal of the Association of Information Systems, and others. He serves as senior editor for the Information Systems Journal and the European Journal of Information Systems.

Clayton A. Looney

Clayton A. Looney ([email protected]) is a professor of management information systems, chair of the MIS Department, and director of the Masters of Science in Business Analytics program in the School of Business at the University of Montana. Leveraging expertise in human–computer interaction, cognitive psychology, and behavioral economics, his cross-disciplinary research focuses on designing technologies to overcome decision-making biases. His work has appeared in Management Science, Journal of Management Information Systems, Organizational Behavior and Human Decision Processes, Decision Sciences, Information Systems Journal, and others.

Gregory D. Moody

Gregory D. Moody ([email protected]) is Lee Professor of Information Systems (IS) and director of the M.S. in MIS program in the Lee Business School at the University of Nevada, Las Vegas. He holds Ph.D. degrees from University of Pittsburgh and University of Oulu, Finland. His interests include IS security and privacy, e-business, and human–computer interaction. His work appears in Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Journal of the Association of Information Systems, European Journal of Information Systems, and others. He is an associate editor of Information Systems Journal and associate and managing editor of AIS Transactions on Human–Computer Interaction.